Mills, D.J., Bossley, K.M., Brown, M. and Harris, C.J.
Towards Parsimonious High-Dimensional Neurofuzzy Systems.
World Congress on Neural Networks
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A neurofuzzy system combines the positive attributes of a neural network and a fuzzy system by providing a transparent framework for representing linguistic rules with well defined modelling and learning characteristics. Unfortunately, their application is limited to problems involving a small number of input variables by the curse of dimensionality where the the size of the rule base and the training set increase as an exponential function of the input dimension. The curse can be alleviated by exploiting structure whereby the function to be approximated is additively decomposed into a series of smaller submodels each of which can be viewed as a conventional neurofuzzy system.
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